Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
This work addresses the labor-intensive task of disaster damage assessment for satellite imagery analysts, though it appears incremental as it builds on existing datasets and challenges.
The authors tackled building damage assessment in satellite imagery by developing problem framing, data processing, and training procedures, achieving substantial improvements over baseline models and scoring among top results on the xView2 challenge leaderboard.
Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts. In the occurrences of natural disasters, timely change detection can save lives. In this work, we report findings on problem framing, data processing and training procedures which are specifically helpful for the task of building damage assessment using the newly released xBD dataset. Our insights lead to substantial improvement over the xBD baseline models, and we score among top results on the xView2 challenge leaderboard. We release our code used for the competition.